Abstract: Methods and systems are presented for providing data consistency in a distributed data storage system using an eventual consistency model. The distributed data storage system may store data across multiple data servers. To process a request for writing a first data value for a data field, a first data server may generate, for the first data value, a first causality chain representing a data replacement history for the data field leading to the first data value. The first data server may insert the first data value without deleting pre-existing data values from the data field. To process a data read request, multiple data values corresponding to the data field may be retrieved. The first data server may then select one data value based on the causality chains associated with the multiple data values for responding to the data read request.

Abstract: There are provided systems and methods for intelligent clustering of account communities for account feature adjustment. A user may utilize an online account with the service provider to process transactions electronic. However, initially the account may be unverified or otherwise untrusted, and limits on electronic transaction processing may be imposed on the account. To provide intelligent feature adjustment, the service provider may utilize a machine learning technique to cluster verified accounts into communities based on their data representations. Thereafter, when a transaction by an unverified account violates a limit imposed on the unverified account, the service provider may process the unverified account using a machine learning model trained for account correlations to the community clusters in order to determine a corresponding community cluster of accounts and adjust a feature based on behaviors and traits of the verified accounts in the community cluster.

Abstract: Methods and systems are presented for providing data consistency in a distributed data storage system using an eventual consistency model. The distributed data storage system may store data across multiple data servers. To process a request for writing a first data value for a data field, a first data server may generate, for the first data value, a first causality chain representing a data replacement history for the data field leading to the first data value. The first data server may insert the first data value without deleting pre-existing data values from the data field. To process a data read request, multiple data values corresponding to the data field may be retrieved. The first data server may then select one data value based on the causality chains associated with the multiple data values for responding to the data read request.

Abstract: Methods and systems are presented for providing data consistency in a distributed data storage system using an eventual consistency model. The distributed data storage system may store data across multiple data servers. To process a request for writing a first data value for a data field, a first data server may generate, for the first data value, a first causality chain representing a data replacement history for the data field leading to the first data value. The first data server may insert the first data value without deleting pre-existing data values from the data field. To process a data read request, multiple data values corresponding to the data field may be retrieved. The first data server may then select one data value based on the causality chains associated with the multiple data values for responding to the data read request.

Abstract: Methods and systems are presented for providing data consistency in a distributed data storage system using an eventual consistency model. The distributed data storage system may store data across multiple data servers. To process a request for writing a first data value for a data field, a first data server may generate, for the first data value, a first causality chain representing a data replacement history for the data field leading to the first data value. The first data server may insert the first data value without deleting pre-existing data values from the data field. To process a data read request, multiple data values corresponding to the data field may be retrieved. The first data server may then select one data value based on the causality chains associated with the multiple data values for responding to the data read request.